1. Sentinel-1 Next Generation – what we know so far
Sentinel-1 is arguably the most important SAR mission in the world. Thanks to its free and open data policy and fine temporal resolution it is the de facto standard of SAR for many. Therefore, the future of the mission is of great interest.
We know that the C and D units of the mission will be almost exact copies of the currently operational A and B units, but things are getting more interesting with the E and F satellites, which correspond to the next generation (Sentinel-1 NG). Not much is known about this potentially game changing mission for Earth Observation world, but there are still some documents. DLR has published a set of slides and a short document based on their Phase-0 study, available from here and here.
We can read that the spatial resolution is planned to be updated from 5 by 22 m to 5 by 5 m. Area-wise it means about 5-times denser data, repeat cycle is planned to be updated from 6 to 4 days. Perhaps the most interesting and significant update concerns the fully polarimetric mode. The system could work in either, dual. pol. mode with 400 km swath or with 280 km swath in quad pol. mode. Concerning the land applications in agriculture that KappaZeta has worked so far, the latter mode is definitely more interesting. Sentinel-1 temporal resolution is already great. With 400 km swath and 4-day revisit it will be even greater – this is not the place where the agricultural applications face the trouble. In some applications, for example, we use only the ascending orbits’ data, neglecting half of the Sentinel-1 temporal data density.
The bigger problem is the information richness of the data. Sentinel-1 dual pol. data with 4 variables per pixel offers just a small subset of applications, which are possible in the SAR world. With quad pol. data there are 8 variables per pixels, though some of them are redundant, you don’t need to be Einstein to understand that more linearly independent input variables are good. In the world of AI and data science, this translates to a richer food for the AI models to eat, which means more accurate information for the end users. Some applications currently impossible would be enabled with the emergence of quad pol. SAR data in large quantities.
An example of how the quad pol. SAR image would look like is shown in the Figure 1 nearby. This is a RADARSAT-2 image with very similar spatial resolution. Unlike tiny amounts of specifically ordered acquisitions of RADARSAT-2, Sentinel-1 NG would start to produce this data in large scale with free and open data policy. We really hope that the 280 km swath quad. pol. mode will be the default one for Sentinel-1 NG. The temporal resolution of Sentinel-1 is already great, but the information acquired could be much richer.
We hope to write about it in a longer blog post in KappaZeta web in the near future. Though it will not happen very soon (launch of the mission is around 2028?), Sentinel-1 NG will be a great game changer in the Earth Observation world. But not only this – it will improve the understanding of our planet in global, regional and local scale – without it, it is difficult to imagine the transition to green economy.
Kaupo Voormansik, SAR expert, CEO
2. Our crop monitoring service prototype is live!
In the beginning of May we launched our crop monitoring prototype service. Three farmers and Estonian Crop Institute are the first to test our service in Estonia and give us valuable feedback to continuously improve the product. Crop monitoring is the first phase of the Harvesting Time Recommendation service prototype, which we are planning to launch in July with same participants.
In crop monitoring service the farmers can see the ongoing season timeseries of Sentinel-1 and Sentinel-2 field-based features and NDVI/RGB/NRG images which are being updated as soon as new images are available. We have also included main weather parameters (daily temperature and precipitation) and are daily updating accumulated agrometeorological parameters like active temperature sum and effective temperature sum. Here are some screenshots from our live service:
We are ready to set up similar service anywhere in the world, so if you are interested in demo or want more information about the prototype then contact us.
Mihkel Järveoja, HaTRY project leader
3. Synthetic NDVI for proxy biomass calculations
Plant biomass is known to be a huge indicator of the ecological state of an area covered by vegetation, providing insights into the energy stored in plants and resulting biofuel available, grazing capacities of fields, amongst others. However, measuring biomass, especially over large areas is a costly process and hence proxy measurements/models have been developed to compute these estimates, one of which uses the Normalized Difference Vegetation Index (NDVI).
The ESA’s Sentinel-2 satellite provides this NDVI data openly (computed from the red and near-infrared bands), with a 5-day revisit time. However, as with any optical satellite data, this is often obstructed by clouds, leaving very little to be seen and analyzed. Sentinel-1 data however is a more faithful data source, with lower resolutions nonetheless on the backscattered radio waves. Hence, we developed an MVP in a month to explore the modeling of NDVI from Sentinel-1 data using Generative Models.
Pictured above, we discovered that this modeling was possible using Multi-Temporal CGANs (Conditional Generative Adversarial Networks), but there is still some work to be done with enhancing the quality of images generated and enforcing temporal constraints on the synthetic images more strictly. Work beyond this MVP resumes from June and we are excited about the results with more data from Sentinel-1, additional preprocessing and model improvements.
Hudson Taylor Lekunze, data analyst
